Many services offered by a mobile communication network depend on the ability to estimate the geographic position or location of mobile terminals in that network. Navigational assistance, location-aware advertising, social networking, and emergency services are just a few examples. Some of these location services (LCS), like emergency services, demand that a mobile terminal's position be estimated very accurately and very quickly, while others, for example social networking, are less stringent. Typically, then, an entity that requests an estimate of the mobile terminal's position (which may be the mobile terminal itself, or some other entity) specifies exactly how accurate that estimate should be, and how quickly the estimate should be provided, depending on the services for which that estimate will be used.
A number of methods for estimating a mobile terminal's position responsive to a request for that position may be available at any given time, some capable of estimating the position with a better accuracy or response time than others. Methods based on Assisted Global Navigation Satellite System (A-GNSS), for example, rely on a satellite navigation system with the assistance of the mobile communication network to estimate a mobile terminal's position with great accuracy (e.g., within a few meters), albeit with a relatively long response time (e.g., several seconds). By contrast, methods based on Cell ID (CID) simply approximate the mobile terminal's position as being the position of the serving base station and thereby estimate the mobile terminal's position with a short response time, but with poor accuracy. Other positioning methods, such as Time Difference of Arrival (TDOA), Fingerprinting, Angle of Arrival (AoA), Enhanced Cell ID (E-CID), etc., generally fall somewhere between A-GNSS and CID in terms of accuracy and response time.
Accordingly, the task of estimating a mobile terminal's position responsive to a request often entails dynamically selecting from among several available positioning methods the method that satisfies the requested accuracy, the requested response time, and any other requested parameter related to the quality of service (QoS) with which the position is estimated. Known approaches to this selection proceed in a piecemeal fashion, with a one-by-one check of requested QoS parameters against the corresponding QoS parameters of the available positioning methods. That is, the response time of each available positioning method is compared to the requested response time, the accuracy of each method is compared to the requested accuracy, and so on.
Some approaches strictly guarantee all requested QoS parameters. In these approaches, the selected positioning method is the one that fully satisfies all of the requested QoS parameters (e.g., according to the one-by-one check, the method's response time fully satisfies the requested response time and the method's accuracy fully satisfies the requested accuracy). If no method is fully satisfactory, none is selected.
Other approaches guarantee just one requested QoS parameter, and provide a “best effort” with regard to the remaining requested QoS parameters. If the service for which the request is being made is defined as accuracy-critical, for example, these approaches select from among those positioning methods that fully satisfy the requested accuracy, the method that best satisfies the requested response time. On the other hand, if the service for which the request is being made is defined as time-critical, the approaches select from among those positioning methods that fully satisfy the requested response time, the method that best satisfies the requested accuracy.
With positioning method selection approached in these ways, position-based services must be rigidly defined as accuracy-critical, time-critical, or both. This rigid definition is sometimes artificial, though, because some services are in actuality neither accuracy-critical nor time-critical; they may instead just require some minimum combination of accuracy and response time. Known approaches to positioning method selection are therefore inflexible and incapable of intelligently selecting a positioning method based on actual QoS requirements of position-based services.
Known approaches also fail to optimally estimate a mobile terminal's position using multiple positioning methods. Specifically, if the positioning attempt fails with the initially selected positioning method, known approaches re-attempt positioning by selecting a different positioning method. Selection of this different positioning method proceeds in the same manner as described above with respect to the initial selection and occurs independently from the initial selection, except that the same positioning method is not again selected. If the re-attempt also fails, yet another positioning method is selected, and so on. In this way, known approaches create an ad hoc sequence of positioning methods by successively selecting individual positioning methods.
Even though each individual positioning method in the resulting sequence is determined, at the time of its selection, to best satisfy the request, the overall resulting sequence may not be the best sequence to satisfy the request. For example, each individual positioning method in the sequence is selected on the assumption that it will succeed in satisfying the request; a positioning method expected to fail is not selected. However, a positioning method expected to fail may actually be part of the sequence that best satisfies the request, since partial results from that method may be more valuable to subsequently selected methods than partial results from a method assumed to succeed.
Furthermore, known approaches fail to account for or otherwise take advantage of parallel performance of multiple positioning methods, which is possible in the LTE Positioning Protocol (LPP). Indeed, the approaches successively select positioning methods based on the assumption that the methods will be performed serially.